Overview

Dataset statistics

Number of variables14
Number of observations584033
Missing cells163074
Missing cells (%)2.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory62.4 MiB
Average record size in memory112.0 B

Variable types

Numeric10
Categorical4

Warnings

Call_Start has a high cardinality: 569222 distinct values High cardinality
Party_Name has a high cardinality: 147 distinct values High cardinality
QueuedTime is highly correlated with WaitTime and 1 other fieldsHigh correlation
RingTime is highly correlated with WaitTime vs QueuedTimeHigh correlation
WaitTime is highly correlated with QueuedTime and 1 other fieldsHigh correlation
Queue + Ring is highly correlated with QueuedTime and 1 other fieldsHigh correlation
WaitTime vs QueuedTime is highly correlated with RingTimeHigh correlation
Party_Name has 47221 (8.1%) missing values Missing
TalkTime has 32386 (5.5%) missing values Missing
AgentTime has 83467 (14.3%) missing values Missing
Call_Start is uniformly distributed Uniform
df_index has unique values Unique
QueuedTime has 35074 (6.0%) zeros Zeros
RingTime has 34598 (5.9%) zeros Zeros
TalkTime has 23689 (4.1%) zeros Zeros
HoldTime has 489220 (83.8%) zeros Zeros
WrapTime has 71372 (12.2%) zeros Zeros
WaitTime vs QueuedTime has 34598 (5.9%) zeros Zeros

Reproduction

Analysis started2021-02-28 17:17:10.843878
Analysis finished2021-02-28 17:18:03.190205
Duration52.35 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct584033
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean308375.6406
Minimum0
Maximum613887
Zeros1
Zeros (%)< 0.1%
Memory size4.5 MiB
2021-02-28T18:18:03.478011image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30083.6
Q1155568
median309931
Q3461700
95-th percentile583704.4
Maximum613887
Range613887
Interquartile range (IQR)306132

Descriptive statistics

Standard deviation177478.9503
Coefficient of variation (CV)0.5755284367
Kurtosis-1.194926169
Mean308375.6406
Median Absolute Deviation (MAD)153050
Skewness-0.01864957845
Sum1.801015505 × 1011
Variance3.14987778 × 1010
MonotocityStrictly increasing
2021-02-28T18:18:03.676489image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
848601
 
< 0.1%
725661
 
< 0.1%
705191
 
< 0.1%
930481
 
< 0.1%
910011
 
< 0.1%
971461
 
< 0.1%
950991
 
< 0.1%
828131
 
< 0.1%
807541
 
< 0.1%
Other values (584023)584023
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
ValueCountFrequency (%)
6138871
< 0.1%
6138861
< 0.1%
6138851
< 0.1%
6138841
< 0.1%
6138831
< 0.1%

Call_Start
Categorical

HIGH CARDINALITY
UNIFORM

Distinct569222
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Memory size4.5 MiB
2020-08-03 11:15:51.000002
 
5
2020-09-15 09:18:49.999999
 
4
2020-04-21 15:18:29.999998
 
4
2020-10-07 11:17:35.000002
 
4
2020-07-07 11:11:22.999998
 
4
Other values (569217)
584012 

Length

Max length26
Median length26
Mean length26
Min length26

Characters and Unicode

Total characters15184858
Distinct characters14
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique554702 ?
Unique (%)95.0%

Sample

1st row2020-01-01 08:22:41.999998
2nd row2020-01-01 08:54:11.999998
3rd row2020-01-01 10:41:51.999997
4th row2020-01-01 10:47:42.999996
5th row2020-01-01 11:06:46.000000
ValueCountFrequency (%)
2020-08-03 11:15:51.0000025
 
< 0.1%
2020-09-15 09:18:49.9999994
 
< 0.1%
2020-04-21 15:18:29.9999984
 
< 0.1%
2020-10-07 11:17:35.0000024
 
< 0.1%
2020-07-07 11:11:22.9999984
 
< 0.1%
2020-05-05 11:52:03.9999964
 
< 0.1%
2020-08-14 13:48:41.9999994
 
< 0.1%
2020-05-13 13:35:56.0000033
 
< 0.1%
2020-05-11 16:17:28.0000003
 
< 0.1%
2020-11-02 08:07:14.0000023
 
< 0.1%
Other values (569212)583995
> 99.9%
2021-02-28T18:18:05.784822image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-09-083621
 
0.3%
2020-06-013458
 
0.3%
2020-05-263432
 
0.3%
2020-08-033185
 
0.3%
2020-06-153182
 
0.3%
2020-05-183128
 
0.3%
2020-08-173121
 
0.3%
2020-08-243114
 
0.3%
2020-09-143101
 
0.3%
2020-07-273097
 
0.3%
Other values (52678)1135627
97.2%

Most occurring characters

ValueCountFrequency (%)
04130772
27.2%
21929506
12.7%
91554110
 
10.2%
11441657
 
9.5%
-1168066
 
7.7%
:1168066
 
7.7%
584033
 
3.8%
.584033
 
3.8%
3567830
 
3.7%
4536670
 
3.5%
Other values (4)1520115
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11680660
76.9%
Other Punctuation1752099
 
11.5%
Dash Punctuation1168066
 
7.7%
Space Separator584033
 
3.8%

Most frequent character per category

ValueCountFrequency (%)
04130772
35.4%
21929506
16.5%
91554110
 
13.3%
11441657
 
12.3%
3567830
 
4.9%
4536670
 
4.6%
5489844
 
4.2%
8359004
 
3.1%
7345896
 
3.0%
6325371
 
2.8%
ValueCountFrequency (%)
:1168066
66.7%
.584033
33.3%
ValueCountFrequency (%)
-1168066
100.0%
ValueCountFrequency (%)
584033
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common15184858
100.0%

Most frequent character per script

ValueCountFrequency (%)
04130772
27.2%
21929506
12.7%
91554110
 
10.2%
11441657
 
9.5%
-1168066
 
7.7%
:1168066
 
7.7%
584033
 
3.8%
.584033
 
3.8%
3567830
 
3.7%
4536670
 
3.5%
Other values (4)1520115
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII15184858
100.0%

Most frequent character per block

ValueCountFrequency (%)
04130772
27.2%
21929506
12.7%
91554110
 
10.2%
11441657
 
9.5%
-1168066
 
7.7%
:1168066
 
7.7%
584033
 
3.8%
.584033
 
3.8%
3567830
 
3.7%
4536670
 
3.5%
Other values (4)1520115
 
10.0%

Exit_Reason
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.5 MiB
AgentAnswered
536828 
Abandoned
 
43483
Redirected
 
3722

Length

Max length13
Median length13
Mean length12.68306928
Min length9

Characters and Unicode

Total characters7407331
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAbandoned
2nd rowAbandoned
3rd rowAbandoned
4th rowAbandoned
5th rowAbandoned
ValueCountFrequency (%)
AgentAnswered536828
91.9%
Abandoned43483
 
7.4%
Redirected3722
 
0.6%
2021-02-28T18:18:06.318667image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-28T18:18:06.465247image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
agentanswered536828
91.9%
abandoned43483
 
7.4%
redirected3722
 
0.6%

Most occurring characters

ValueCountFrequency (%)
e1665133
22.5%
n1160622
15.7%
A1117139
15.1%
d631238
 
8.5%
t540550
 
7.3%
r540550
 
7.3%
g536828
 
7.2%
s536828
 
7.2%
w536828
 
7.2%
b43483
 
0.6%
Other values (5)98132
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6286470
84.9%
Uppercase Letter1120861
 
15.1%

Most frequent character per category

ValueCountFrequency (%)
e1665133
26.5%
n1160622
18.5%
d631238
 
10.0%
t540550
 
8.6%
r540550
 
8.6%
g536828
 
8.5%
s536828
 
8.5%
w536828
 
8.5%
b43483
 
0.7%
a43483
 
0.7%
Other values (3)50927
 
0.8%
ValueCountFrequency (%)
A1117139
99.7%
R3722
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin7407331
100.0%

Most frequent character per script

ValueCountFrequency (%)
e1665133
22.5%
n1160622
15.7%
A1117139
15.1%
d631238
 
8.5%
t540550
 
7.3%
r540550
 
7.3%
g536828
 
7.2%
s536828
 
7.2%
w536828
 
7.2%
b43483
 
0.6%
Other values (5)98132
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII7407331
100.0%

Most frequent character per block

ValueCountFrequency (%)
e1665133
22.5%
n1160622
15.7%
A1117139
15.1%
d631238
 
8.5%
t540550
 
7.3%
r540550
 
7.3%
g536828
 
7.2%
s536828
 
7.2%
w536828
 
7.2%
b43483
 
0.6%
Other values (5)98132
 
1.3%

Party_Name
Categorical

HIGH CARDINALITY
MISSING

Distinct147
Distinct (%)< 0.1%
Missing47221
Missing (%)8.1%
Memory size4.5 MiB
Alex Dillon
 
16303
Daniel Schirmer
 
13539
Dave Lee Wincek
 
13027
Timmy Moran
 
12643
John Gene Vura
 
12563
Other values (142)
468737 

Length

Max length17
Median length16
Mean length15.37533438
Min length8

Characters and Unicode

Total characters8253664
Distinct characters51
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowKyle Younglas
2nd rowLuis Torres
3rd rowDaniel Schirmer
4th rowMike Bilfield
5th rowChris Smucny
ValueCountFrequency (%)
Alex Dillon 16303
 
2.8%
Daniel Schirmer 13539
 
2.3%
Dave Lee Wincek13027
 
2.2%
Timmy Moran 12643
 
2.2%
John Gene Vura12563
 
2.2%
Alex Baltas 12466
 
2.1%
Butch Herten 11823
 
2.0%
Mike Pascaru 11626
 
2.0%
Alex Hord 11000
 
1.9%
Dustin Pollock 10975
 
1.9%
Other values (137)410847
70.3%
(Missing)47221
 
8.1%
2021-02-28T18:18:06.874555image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
alex56828
 
5.2%
mike43851
 
4.0%
john25117
 
2.3%
matt21582
 
2.0%
dan21579
 
2.0%
dillon19747
 
1.8%
joe18440
 
1.7%
connor17411
 
1.6%
daniel15998
 
1.5%
schirmer15997
 
1.5%
Other values (146)842664
76.7%

Most occurring characters

ValueCountFrequency (%)
2121807
25.7%
e690761
 
8.4%
a554492
 
6.7%
n493906
 
6.0%
r427038
 
5.2%
l386094
 
4.7%
o354832
 
4.3%
i353059
 
4.3%
t245160
 
3.0%
h183199
 
2.2%
Other values (41)2443316
29.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5012556
60.7%
Space Separator2121807
25.7%
Uppercase Letter1118900
 
13.6%
Other Punctuation401
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
e690761
13.8%
a554492
11.1%
n493906
9.9%
r427038
 
8.5%
l386094
 
7.7%
o354832
 
7.1%
i353059
 
7.0%
t245160
 
4.9%
h183199
 
3.7%
c181461
 
3.6%
Other values (15)1142554
22.8%
ValueCountFrequency (%)
M152073
13.6%
S101135
9.0%
D100561
9.0%
B96273
 
8.6%
A91910
 
8.2%
C89153
 
8.0%
J76684
 
6.9%
T62976
 
5.6%
K61982
 
5.5%
L39264
 
3.5%
Other values (14)246889
22.1%
ValueCountFrequency (%)
2121807
100.0%
ValueCountFrequency (%)
*401
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6131456
74.3%
Common2122208
 
25.7%

Most frequent character per script

ValueCountFrequency (%)
e690761
 
11.3%
a554492
 
9.0%
n493906
 
8.1%
r427038
 
7.0%
l386094
 
6.3%
o354832
 
5.8%
i353059
 
5.8%
t245160
 
4.0%
h183199
 
3.0%
c181461
 
3.0%
Other values (39)2261454
36.9%
ValueCountFrequency (%)
2121807
> 99.9%
*401
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII8253664
100.0%

Most frequent character per block

ValueCountFrequency (%)
2121807
25.7%
e690761
 
8.4%
a554492
 
6.7%
n493906
 
6.0%
r427038
 
5.2%
l386094
 
4.7%
o354832
 
4.3%
i353059
 
4.3%
t245160
 
3.0%
h183199
 
2.2%
Other values (41)2443316
29.6%

channel
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.5 MiB
SEO
430987 
PPC
153046 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1752099
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSEO
2nd rowSEO
3rd rowSEO
4th rowSEO
5th rowSEO
ValueCountFrequency (%)
SEO430987
73.8%
PPC153046
 
26.2%
2021-02-28T18:18:07.257098image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-28T18:18:07.396705image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
seo430987
73.8%
ppc153046
 
26.2%

Most occurring characters

ValueCountFrequency (%)
S430987
24.6%
E430987
24.6%
O430987
24.6%
P306092
17.5%
C153046
 
8.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1752099
100.0%

Most frequent character per category

ValueCountFrequency (%)
S430987
24.6%
E430987
24.6%
O430987
24.6%
P306092
17.5%
C153046
 
8.7%

Most occurring scripts

ValueCountFrequency (%)
Latin1752099
100.0%

Most frequent character per script

ValueCountFrequency (%)
S430987
24.6%
E430987
24.6%
O430987
24.6%
P306092
17.5%
C153046
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1752099
100.0%

Most frequent character per block

ValueCountFrequency (%)
S430987
24.6%
E430987
24.6%
O430987
24.6%
P306092
17.5%
C153046
 
8.7%

QueuedTime
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct189
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.31408328
Minimum0
Maximum188
Zeros35074
Zeros (%)6.0%
Memory size4.5 MiB
2021-02-28T18:18:07.569454image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median4
Q313
95-th percentile77
Maximum188
Range188
Interquartile range (IQR)10

Descriptive statistics

Standard deviation27.83661022
Coefficient of variation (CV)1.817713128
Kurtosis10.81378292
Mean15.31408328
Median Absolute Deviation (MAD)2
Skewness3.158660053
Sum8943930
Variance774.8768683
MonotocityNot monotonic
2021-02-28T18:18:07.774413image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4122481
21.0%
374541
12.8%
256806
 
9.7%
536638
 
6.3%
136112
 
6.2%
035074
 
6.0%
612844
 
2.2%
711634
 
2.0%
911370
 
1.9%
811119
 
1.9%
Other values (179)175414
30.0%
ValueCountFrequency (%)
035074
 
6.0%
136112
 
6.2%
256806
9.7%
374541
12.8%
4122481
21.0%
ValueCountFrequency (%)
18825
 
< 0.1%
18744
< 0.1%
18640
< 0.1%
18561
< 0.1%
18471
< 0.1%

RingTime
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.620021471
Minimum0
Maximum48
Zeros34598
Zeros (%)5.9%
Memory size4.5 MiB
2021-02-28T18:18:07.954952image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median7
Q310
95-th percentile16
Maximum48
Range48
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.585284004
Coefficient of variation (CV)0.6017416121
Kurtosis0.286879361
Mean7.620021471
Median Absolute Deviation (MAD)3
Skewness0.6253447648
Sum4450344
Variance21.0248294
MonotocityNot monotonic
2021-02-28T18:18:08.157212image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
657029
9.8%
554987
 
9.4%
751720
 
8.9%
450194
 
8.6%
849708
 
8.5%
943935
 
7.5%
1036863
 
6.3%
335122
 
6.0%
034598
 
5.9%
1129988
 
5.1%
Other values (23)139889
24.0%
ValueCountFrequency (%)
034598
5.9%
18918
 
1.5%
222140
3.8%
335122
6.0%
450194
8.6%
ValueCountFrequency (%)
481
< 0.1%
431
< 0.1%
341
< 0.1%
292
< 0.1%
281
< 0.1%

TalkTime
Real number (ℝ≥0)

MISSING
ZEROS

Distinct922
Distinct (%)0.2%
Missing32386
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean176.8936874
Minimum0
Maximum921
Zeros23689
Zeros (%)4.1%
Memory size4.5 MiB
2021-02-28T18:18:08.344126image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q143
median129
Q3243
95-th percentile557
Maximum921
Range921
Interquartile range (IQR)200

Descriptive statistics

Standard deviation174.5015169
Coefficient of variation (CV)0.9864767895
Kurtosis2.388564124
Mean176.8936874
Median Absolute Deviation (MAD)94
Skewness1.555612029
Sum97582872
Variance30450.77939
MonotocityNot monotonic
2021-02-28T18:18:08.587350image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
023689
 
4.1%
204149
 
0.7%
194034
 
0.7%
184022
 
0.7%
214008
 
0.7%
223876
 
0.7%
173871
 
0.7%
233744
 
0.6%
243723
 
0.6%
163692
 
0.6%
Other values (912)492839
84.4%
(Missing)32386
 
5.5%
ValueCountFrequency (%)
023689
4.1%
11770
 
0.3%
21963
 
0.3%
31814
 
0.3%
41531
 
0.3%
ValueCountFrequency (%)
92128
< 0.1%
92024
< 0.1%
91927
< 0.1%
91816
< 0.1%
91732
< 0.1%

HoldTime
Real number (ℝ≥0)

ZEROS

Distinct156
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.030650665
Minimum0
Maximum155
Zeros489220
Zeros (%)83.8%
Memory size4.5 MiB
2021-02-28T18:18:08.777434image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile20
Maximum155
Range155
Interquartile range (IQR)0

Descriptive statistics

Standard deviation17.4222695
Coefficient of variation (CV)4.322445915
Kurtosis31.49375239
Mean4.030650665
Median Absolute Deviation (MAD)0
Skewness5.434796955
Sum2354033
Variance303.5354746
MonotocityNot monotonic
2021-02-28T18:18:08.981668image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0489220
83.8%
121291
 
3.6%
210392
 
1.8%
38147
 
1.4%
45553
 
1.0%
54174
 
0.7%
63443
 
0.6%
72646
 
0.5%
82040
 
0.3%
91386
 
0.2%
Other values (146)35741
 
6.1%
ValueCountFrequency (%)
0489220
83.8%
121291
 
3.6%
210392
 
1.8%
38147
 
1.4%
45553
 
1.0%
ValueCountFrequency (%)
15564
< 0.1%
15473
< 0.1%
15387
< 0.1%
15274
< 0.1%
15169
< 0.1%

WrapTime
Real number (ℝ≥0)

ZEROS

Distinct1004
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.1054666
Minimum0
Maximum1003
Zeros71372
Zeros (%)12.2%
Memory size4.5 MiB
2021-02-28T18:18:09.180049image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median62
Q3171
95-th percentile442
Maximum1003
Range1003
Interquartile range (IQR)163

Descriptive statistics

Standard deviation158.4519334
Coefficient of variation (CV)1.319273284
Kurtosis6.101507893
Mean120.1054666
Median Absolute Deviation (MAD)59
Skewness2.238245217
Sum70145556
Variance25107.01519
MonotocityNot monotonic
2021-02-28T18:18:09.539240image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
071372
 
12.2%
414652
 
2.5%
513638
 
2.3%
313119
 
2.2%
611673
 
2.0%
79744
 
1.7%
88618
 
1.5%
97370
 
1.3%
106641
 
1.1%
115857
 
1.0%
Other values (994)421349
72.1%
ValueCountFrequency (%)
071372
12.2%
11952
 
0.3%
25538
 
0.9%
313119
 
2.2%
414652
 
2.5%
ValueCountFrequency (%)
100316
< 0.1%
100222
< 0.1%
100115
< 0.1%
100017
< 0.1%
99912
< 0.1%

WaitTime
Real number (ℝ≥0)

HIGH CORRELATION

Distinct189
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.93410475
Minimum0
Maximum188
Zeros1839
Zeros (%)0.3%
Memory size4.5 MiB
2021-02-28T18:18:09.752858image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q110
median14
Q322
95-th percentile81
Maximum188
Range188
Interquartile range (IQR)12

Descriptive statistics

Standard deviation26.9169826
Coefficient of variation (CV)1.173666158
Kurtosis10.86937574
Mean22.93410475
Median Absolute Deviation (MAD)5
Skewness3.11753594
Sum13394274
Variance724.5239525
MonotocityNot monotonic
2021-02-28T18:18:09.958030image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1043238
 
7.4%
940832
 
7.0%
1238079
 
6.5%
1137322
 
6.4%
836780
 
6.3%
1431381
 
5.4%
1329053
 
5.0%
727199
 
4.7%
1623353
 
4.0%
1521062
 
3.6%
Other values (179)255734
43.8%
ValueCountFrequency (%)
01839
 
0.3%
1842
 
0.1%
21286
 
0.2%
32095
 
0.4%
46000
1.0%
ValueCountFrequency (%)
18894
< 0.1%
187117
< 0.1%
18695
< 0.1%
185114
< 0.1%
184102
< 0.1%

Queue + Ring
Real number (ℝ≥0)

HIGH CORRELATION

Distinct189
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.93410475
Minimum0
Maximum188
Zeros1839
Zeros (%)0.3%
Memory size4.5 MiB
2021-02-28T18:18:10.148035image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q110
median14
Q322
95-th percentile81
Maximum188
Range188
Interquartile range (IQR)12

Descriptive statistics

Standard deviation26.9169826
Coefficient of variation (CV)1.173666158
Kurtosis10.86937574
Mean22.93410475
Median Absolute Deviation (MAD)5
Skewness3.11753594
Sum13394274
Variance724.5239525
MonotocityNot monotonic
2021-02-28T18:18:10.346925image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1043238
 
7.4%
940832
 
7.0%
1238079
 
6.5%
1137322
 
6.4%
836780
 
6.3%
1431381
 
5.4%
1329053
 
5.0%
727199
 
4.7%
1623353
 
4.0%
1521062
 
3.6%
Other values (179)255734
43.8%
ValueCountFrequency (%)
01839
 
0.3%
1842
 
0.1%
21286
 
0.2%
32095
 
0.4%
46000
1.0%
ValueCountFrequency (%)
18894
< 0.1%
187117
< 0.1%
18695
< 0.1%
185114
< 0.1%
184102
< 0.1%

WaitTime vs QueuedTime
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.620021471
Minimum0
Maximum48
Zeros34598
Zeros (%)5.9%
Memory size4.5 MiB
2021-02-28T18:18:10.537103image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median7
Q310
95-th percentile16
Maximum48
Range48
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.585284004
Coefficient of variation (CV)0.6017416121
Kurtosis0.286879361
Mean7.620021471
Median Absolute Deviation (MAD)3
Skewness0.6253447648
Sum4450344
Variance21.0248294
MonotocityNot monotonic
2021-02-28T18:18:10.734572image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
657029
9.8%
554987
 
9.4%
751720
 
8.9%
450194
 
8.6%
849708
 
8.5%
943935
 
7.5%
1036863
 
6.3%
335122
 
6.0%
034598
 
5.9%
1129988
 
5.1%
Other values (23)139889
24.0%
ValueCountFrequency (%)
034598
5.9%
18918
 
1.5%
222140
3.8%
335122
6.0%
450194
8.6%
ValueCountFrequency (%)
481
< 0.1%
431
< 0.1%
341
< 0.1%
292
< 0.1%
281
< 0.1%

AgentTime
Real number (ℝ≥0)

MISSING

Distinct1767
Distinct (%)0.4%
Missing83467
Missing (%)14.3%
Infinite0
Infinite (%)0.0%
Mean319.4068694
Minimum0
Maximum2037
Zeros49
Zeros (%)< 0.1%
Memory size4.5 MiB
2021-02-28T18:18:10.921350image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29
Q1120
median252
Q3441
95-th percentile867
Maximum2037
Range2037
Interquartile range (IQR)321

Descriptive statistics

Standard deviation264.9798105
Coefficient of variation (CV)0.829599598
Kurtosis1.963352043
Mean319.4068694
Median Absolute Deviation (MAD)151
Skewness1.350473477
Sum159884219
Variance70214.29996
MonotocityNot monotonic
2021-02-28T18:18:11.148279image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
291598
 
0.3%
281581
 
0.3%
271567
 
0.3%
251542
 
0.3%
331542
 
0.3%
321539
 
0.3%
341537
 
0.3%
301537
 
0.3%
311521
 
0.3%
261520
 
0.3%
Other values (1757)485082
83.1%
(Missing)83467
 
14.3%
ValueCountFrequency (%)
049
 
< 0.1%
121
 
< 0.1%
2103
 
< 0.1%
3269
< 0.1%
4315
0.1%
ValueCountFrequency (%)
20371
< 0.1%
19541
< 0.1%
19341
< 0.1%
19071
< 0.1%
18911
< 0.1%

Interactions

2021-02-28T18:17:36.629193image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:36.881660image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:37.161775image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:37.419983image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:37.689336image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:37.944125image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:38.204552image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:38.489963image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:38.737747image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:39.007232image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:39.275794image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:39.529558image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:39.793716image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:40.072426image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:40.330279image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:40.600026image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:40.864279image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:41.124415image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:41.401085image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:41.661407image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:41.931376image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:42.201131image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:42.455415image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:42.707257image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:42.953308image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:43.206743image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:43.444298image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:43.711599image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:43.955878image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:44.201711image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:44.457566image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:44.738826image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:44.984579image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:45.232063image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:45.479372image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:45.706397image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:45.964514image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:46.224881image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:46.501181image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:46.761561image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:47.023769image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:47.296056image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:47.571835image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:47.850236image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:48.095551image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:48.353558image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:48.610151image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:48.865636image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:49.120866image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:49.368758image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:49.631319image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:49.903396image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:50.167175image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:50.407292image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:50.669418image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:50.916852image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:51.176750image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:51.440884image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:51.698490image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:51.959411image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:52.224746image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:52.498015image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:52.773590image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:53.041885image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:53.319472image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:53.582842image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:53.841905image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:54.113825image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:54.379859image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:54.638463image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:54.928998image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:55.208506image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:55.472718image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:55.719115image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:55.985095image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:56.239138image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:56.494934image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:56.757097image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:57.373070image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:57.619751image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:57.879687image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:58.121904image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:58.364693image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:58.613738image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:58.868883image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:59.112508image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:59.366543image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:59.613256image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:17:59.866743image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T18:18:00.117440image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-02-28T18:18:11.336707image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-02-28T18:18:11.540515image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-02-28T18:18:11.746947image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-02-28T18:18:11.951756image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-02-28T18:18:12.145475image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-02-28T18:18:00.840599image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-28T18:18:01.519598image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-02-28T18:18:02.363976image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-02-28T18:18:02.683925image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexCall_StartExit_ReasonParty_NamechannelQueuedTimeRingTimeTalkTimeHoldTimeWrapTimeWaitTimeQueue + RingWaitTime vs QueuedTimeAgentTime
002020-01-01 08:22:41.999998AbandonedNaNSEO110NaN0.00.011110NaN
112020-01-01 08:54:11.999998AbandonedNaNSEO1150NaN0.00.01151150NaN
222020-01-01 10:41:51.999997AbandonedNaNSEO250NaN0.00.025250NaN
332020-01-01 10:47:42.999996AbandonedNaNSEO600NaN0.00.060600NaN
442020-01-01 11:06:46.000000AbandonedNaNSEO200NaN0.00.020200NaN
552020-01-01 11:25:49.000002AbandonedNaNSEO70NaN0.00.0770NaN
662020-01-01 15:08:38.000002AbandonedNaNSEO230NaN0.00.023230NaN
772020-01-01 16:38:39.000005AbandonedNaNSEO1190NaN0.00.01191190NaN
882020-01-01 16:53:30.999999AbandonedNaNSEO120NaN0.00.012120NaN
992020-01-01 16:58:58.999999AbandonedNaNSEO200NaN0.00.020200NaN

Last rows

df_indexCall_StartExit_ReasonParty_NamechannelQueuedTimeRingTimeTalkTimeHoldTimeWrapTimeWaitTimeQueue + RingWaitTime vs QueuedTimeAgentTime
5840236138782021-02-21 12:07:37.000004AgentAnsweredMatthew DuffSEO1111210.00.00.0222211210.0
5840246138792021-02-21 12:08:27.000004AgentAnsweredJunior FetchetSEO88133.00.02.016168135.0
5840256138802021-02-21 12:13:19.000001AgentAnsweredMatt TashjianSEO151568.00.042.0303015110.0
5840266138812021-02-21 12:13:54.000002AgentAnsweredMatthew DuffSEO18692.00.02.02424694.0
5840276138822021-02-21 12:14:41.999997AgentAnsweredLauren BaschSEO17540.00.00.02222540.0
5840286138832021-02-21 12:17:38.999996AgentAnsweredMatt TashjianSEO1414121.00.02.0282814123.0
5840296138842021-02-21 12:18:55.000003AgentAnsweredMeredith ChesneySEO44289.00.01.0884290.0
5840306138852021-02-21 12:20:36.000004AbandonedNaNSEO80NaN0.00.0880NaN
5840316138862021-02-21 12:22:32.999998AgentAnsweredMatt TashjianSEO171755.00.012.034341767.0
5840326138872021-02-21 12:24:08.999997AgentAnsweredJunior FetchetSEO77201.00.042.014147243.0